Nomagic
AI
ResearchEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Research Engineer at Nomagic. Skills: Robotics, Machine Learning, Large-scale multimodal model training, Python, PyTorch/JAX. Design, implement, and maintain the core infrastructure for large-scale VLA model training, including scheduling, distribution, job management, checkpointing, and rigorous logging. Build the critical tools and abstractions necessary for launching, monitoring, debugging, and seamlessly reproducing complex, multi-variant experiments”
What You'll Achieve.
Build, train, and deploy foundational models that bring our fleet from a classical control stack to generalized AI mastery; Improve our agents' training recipes to directly change the economics of the company; Measure performance by grounding models in deployment; Systematically evaluate model capabilities far beyond the limits of simulation; Measure real-world success rates; Continuously improve our iteration speed
Industry & Context.
Debug failures when software meets the physical world; Identify operational bottlenecks
Play with real robots, solving real problems, every day, Comfort working hands-on with hardware
What They're Looking For.
Must Have
Deep experience and understanding at the intersection of machine learning, systems engineering, and robotics, Experience training, fine-tuning, and deploying modern deep learning architectures (Transformers, VLMs or VLAs, Imitation Learning, RL) for robot control, ideally with policies validated on real hardware, Software engineering and infrastructure skills, Highly proficient in Python and deep learning frameworks (PyTorch/JAX), Can write clean, scalable code for training and evaluation, Comfort working hands-on with hardware, Understand the robotics full stack (perception, controls, state estimation), Know how to debug failures when software meets the physical world, Ability to move seamlessly between research and implementation, Prefer execution, iteration speed, and real-world robustness over theoretical purity
Nice to Have
Expertise in both Robotics and ML and large-scale multimodal model training
What You'll Do.
and maintain the core infrastructure for large-scale VLA model training
Build the critical tools and abstractions necessary for launching
and seamlessly reproducing complex
multi-variant experiments
Utilize massive repository of offline
classical stack data to pre-train robust robot foundation models
Translate core research needs into concrete infra capabilities
and close the loop directly with ML researchers to unblock model progress
Design new robotic tasks and build lightweight physical setups to systematically evaluate model capabilities far beyond the limits of simulation
Ensure robots are properly configured
and ready for rollouts
Coordinate data collection efforts and run structured
on-robot evaluations to measure real-world success rates
Analyze real-world evaluation results to guide the ML research direction
Identify operational bottlenecks across software
and deployment systems to continuously improve iteration speed
Beta test internal and third-party tools for teaching robots new skills
structured documentation so the broader team can reproduce your workflows and scale your impact
How You'll Work.
Team & Collaboration
Close the loop directly with ML researchers to unblock model progress; Coordinate data collection efforts; Write documentation so the broader team can reproduce your workflows and scale your impact
Full Job Description
## Description Do you believe the path to general-purpose physical AI runs through noisy, real-world factory deployments? Are you excited by the challenge of turning the classical robotic stacks into the foundational training data for physical AI? Do you want to bridge the gap between world-class ML research and industrial-scale robotic execution? If your answers are yes, we should talk. At Nomagic, we are executing a humble pivot for general-purpose physical AI. We believe that physical AI is fundamentally a knowledge transfer problem - we are leveraging the "internet data" of robotics - massive deployment logs from real systems operating in production environments - to bootstrap our efforts. We are looking for Research Engineers who will help us to build, train, and deploy foundational models that bring our fleet from a classical control stack to generalized AI mastery. ## Offer essentials Play with real robots, solving real problems, every day. Relocation package. Flexible working hours. English-speaking environment. ## Here is why we love this job ourselves, and hope you will enjoy it too We combine world-class research with top-notch engineering and apply it to solve real problems The data already exists. We have robots in production at scale. We aren't waiting for datasets to be collected; the byproduct of our machines doing useful work is being created right now. We measure what matters. We test our code in unit tests, simulations, and directly on real robots. Grounding our models in deployment allows us to truly measure performance. High leverage, high impact. We’re still a highly focused team. If your training recipes improve our agents, you directly change the economics of the company. World-class peers. Our team has built Google Warsaw, unicorn startups, led research in DeepMind, tested rocket engines, and worked at top companies like Nvidia and ByteDance. Now, we are shaping the reality of Physical AI together. We are building the bridge. We aren't a ne
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